Posts Tagged ‘CTO’

Some of the business ramifications of the $1 trillion in IT debt have been explored in the first post of this two-part analysis. This second post focuses on “an ounce of prevention is worth a pound of cure” aspects of IT debt. In particular, it proposes an explanation why prevention was often neglected in the US over the past decade and very possibly longer. This explanation is not meant to dwell on the past. Rather, it studies the patterns of the past in order to provide guidance for what you could do and should do in the future to rein in technical debt.

The prevention vis-a-vis cure trade-off in software was illustrated by colleague and friend Jim Highsmith in the following figure:

Figure 1: The Technical Debt Curve

As Jim astutely points out, “once on far right of curve all choices are hard.” My experience as well as those of various Cutter colleagues have shown it is actually very hard. The reason is simple: on the far right the software controls you more than you control it. The manifestations of technical debt [1] in the form of pressing customer problems in the production environment force you into a largely reactive mode of operation. This reactive mode of operation is prone to a high error injection rate – you introduce new bugs while you fix old ones. Consequently, progress is agonizingly slow and painful. It is often characterized by “never-ending” testing periods.

In Measure and Manage Your IT Debt, Gartner’s Andrew Kyte put his finger on the mechanics that lead to the accumulation of technical debt – “when budget are tight, maintenance gets cut.” While I do not doubt Andrew’s observation, it does not answer a deeper question: why would maintenance get cut in the face of the consequences depicted in Figure 1? Most CFOs and CEOs I know would get quite alarmed by Figure 1. They do not need to be experts in object-oriented programming in order to take steps to mitigate the risks associated with slipping to the far right of the curve.

The return on assets (ROA) for U.S. firms has steadily fallen to almost one-quarter of 1965 levels.

Similarly, the ROA performance gap between corporate winners and losers has increased over time, with the “winners” barely maintaining previous performance levels while the losers experience rapid performance deterioration.

U.S. competitive intensity has more than doubled during that same time [i.e. the US has become twice as competitive – IG].

Average Lifetime of S&P 500 companies [declined steadily over this period].

However, in those same 40 years, labor productivity has doubled – largely due to advances in technology and business innovation.

Discussion of the full-fledged analysis that Brown derives based on these five facts is beyond the scope of this blog post [2]. However, one of the phenomena he highlights – “The performance paradox: ROA has dropped in the face of increasing labor productivity” – is IMHO at the roots of the staggering IT debt we are staring at.

Put yourself in the shoes of your CFO or your CEO, weighing the five facts highlighted by Brown in the context of Highsmith’s technical debt curve. Unless you are one of the precious few winner companies, the only viable financial strategy you can follow is a margin strategy. You are very competitive (#3 above). You have already ridden the productivity curve (#5 above). However, growth is not demonstrable or not economically feasible given the investment it takes (#1 & #2 above). Needless to say, just thinking about being dropped out of the S&P 500 index sends cold sweat down your spine. The only way left to you to satisfy the quarterly expectations of Wall Street is to cut, cut and cut again anything that does not immediately contribute to your cashflow. You cut on-going refactoring of code even if your CTO and CIO have explained the technical debt curve to you in no uncertain terms. You are not happy to do so but you are willing to pay the price down the road. You are basically following a “survive to fight another day” strategy.

If you accept this explanation for the level of debt we are staring at, the core issue with respect to IT debt at the individual company level [3] is how “patient” (or “impatient”) investment capital is. Studies by Carlota Perez seem to indicate we are entering a phase of the techno-economic cycle in which investment capital will shift from financial speculation toward (the more “patient”) production capital. While this shift is starting to happens, you have the opportunity to apply “an ounce of prevention is worth a pound of cure” strategy with respect to the new code you will be developing.

My recommendation would be to combine technical debt measurements with software process change. The ability to measure technical debt through code analysis is a necessary but not sufficient condition for changing deep-rooted patterns. Once you institute a process policy like “stop the line whenever the level of technical debt rose,” you combine the “necessary” with the “sufficient” by tying the measurement to human behavior. A possible way to do so through a modified Agile/Scrum process is illustrated in Figure 2:

Figure 2: Process Control Model for Controlling Technical Debt

As you can see in Figure 2, you stop the line and convene an event-driven Agile meeting whenever the technical debt of a certain build exceeds that of the previous build. If ‘stopping the line’ with every such build is “too much of a good thing” for your environment, you can adopt statistical process control methods to gauge when the line should be stopped. (See Using 3σ Control Limits in Software Engineering for a discussion of the settings appropriate for your environment.)

An absolutely critical question this analysis does not cover is “Buthow do we pay back our $1 trillion debt?!” I will address this most important question in a forthcoming post which draws upon the threads of this post plus those in the preceding Part I.

Footnotes:

[1] Kyte/Gartner define IT Debt as “the costs for bringing all the elements [i.e. business applications] in the [IT] portfolio up to a reasonable standard of engineering integrity, or replace them.” In essence, IT Debt differs from the definition of Technical Debt used in The Agile Executive in that it accounts for the possible costs associated with replacing an application. For example, the technical debt calculated through doing code analysis on a certain application might amount to $500K. In contrast, the cost of replacement might be $250K, $1M or some other figure that is not necessarily related to intrinsic quality defects in the current code base.

The Cutter Consortium has announced the availability of the Technical Debt Assessment and Valuation Service. The service combines static code analytics with dynamic program analytics to give the client “x-rays” of the software being examined at any desired granularity – from the whole project portfolio to a single instruction. It breaks down technical debt into the areas of coverage, complexity, duplication, violations and comments. Clients get an aggregate dollar figure for “paying back” debt that they can then plug into their financial models to objectively analyze their critical software assets. Based on these metrics, they can make the best decisions about their ongoing strategy for the software development effort under scrutiny.

Executives in charge of software governance have long dealt with two kinds of dollar figures: One, the cost of producing and maintaining the software; and two, the value of the software, which is usually expressed in terms of the net present value associated with the expected value stream the product will generate. Now we can deal with technical debt in the same quantitative manner, regardless of the software methods a company uses.

When expressed in terms of dollars, technical debt ties neatly into value vis-à-vis cost considerations. For a “well behaved” software project, three factors — value, cost, and technical debt — have to satisfy the equation Value >> Cost > Technical Debt. Monitoring the balance between value, cost, and technical debt on an ongoing basis is an effective way for organizations to stay on top of their real progress, and for stakeholders and investors to ensure their investment is sound.

By boiling down technical debt to dollars and tying it to cost and value, the service enables a metrics-driven governance framework for the use of five major constituencies, as follows:

Technical debt assessments and valuation can specifically help CIOs ensure alignment of software development with IT Operations; give CTOs early warning signs of impending project trouble; assure those involved in due diligence for M&A activity that the code being acquired will adapt to meet future needs; enables CEOs to effectively govern the software development process; and, it provides critical information as to whether software under consideration constitutes an asset or a liability for venture capitalists who need to make informed investment decisions.

It should finally be pointed out that the technical debt assessment service and the governance framework it enables are applicable to any software method. They can be used to:

Govern a heterogeneous environment in which multiple software methods are used

Make apples-to-apples comparisons between disparate software projects

Assess project performance vis-a-vis industry norms

Forthcoming Cutter Executive Reports, Executive Updates and Email Advisors on the technical debt service are restricted to Cutter clients. As appropriate, I will publish the latest and greatest news on the subject in the Cutter Blog (which is an open forum I highly recommend).

Acknowledgements: I would like to wholeheartedly thank the following colleagues for inspiring, enlightening and supporting me during the preparation of the service:

Buying (stocks) on margin is broadly recognized as a risky investment strategy. Funding long-term investments with short-term debt exposes the investor to margin calls as he/she might not be able to secure more financing when needed. The resultant margin call is never pleasant.

The accrual of technical debt in the course of aggressively developing functions and features is quite a similar phenomenon. The CTO is betting the functionality he/she is developing will pay off before the need to “pay back” the technical debt becomes imperative. The temptation to do so is particularly strong due to the lack of credit limits on technical debt. For all practical purposes the CTO is “developing on margin.”

In his comprehensive studies of the economics of software, Capers Jones has actually put a 3-5 year ceiling on the economical viability of developing on margin:

Indeed, the economic value of lagging applications is questionable after about three to five years. The degradation of initial structure and the increasing difficulty of making updates without “bad fixes” tends towards negative returns on investment (ROI) within a few years.

As the CEO leading a company, or the venture capitalist funding it, you can restrain development on margin by establishing credit limits. Use a combination of static code analysis with dynamic program analysis to calculate the amount of accrued technical debt in $$ terms. (An illustration of such calculation as well as a breakdown of the technical debt is given in the Sonar chart above). Set a limit (say $0.25 per line of code) on the amount of permitted technical debt. Once the limit is reached, developers are not allowed to continue developing new functionality – they have to first reduce (and hopefully eliminate) their technical debt.

A very simple “Lacmus test” is available to the CEO/VC until the code is instrumented and the analytics illustrated above generated. Ask your CTO about unit test coverage. If the coverage is low (say <30%), chances are the technical debt is high. Whether the CTO realizes it or not, low unit test coverage is a good indicator of technical debt of all kinds. Moreover, the investment required to develop a full-fledged suite of unit tests is often the largest component of the technical debt to be paid back.

“I don’t care about Agile; I care about money!” These words, said to me not too long ago by an executive with whom I was discussing the roll-out of Agile in his division, are the impetus for this post. Whether you are or are not in R&D, Agile can have a significant impact on you and on your company, including the bottom line.

To keep things simple, listed below are a few “front lobe” considerations for CXOs – CEOs, CFOs, CIOs, CMOs, COOs and CTOs. The listed considerations are neither “good” nor “bad” – they are what they are. They are rendered here for the thinking when an Agile topic comes up in the domain(s) you are responsible for.

Some of the considerations for CXO’s are linked to posts, articles or books that already address the subject to some degree. As additional related posts will get rolled out on this blog, they will be linked to the bullets below. In other words, the Agile Considerations for CXOs post will evolve, serving as a quick index for an eclectic list of Agile CXO considerations.